Dispersion of gold in stream sediments in the Sungai Kuli region, Sabah, Malaysia
Bibliographic record
Abstract
The distribution of Au has been investigated under tropical rainforest conditions in an Au-rich tributary of the Sungai Kuamut, Sabah, Malaysia. Paired high- and low-energy sediment samples were collected, before disturbance by logging, from gravel and cobble sites on bars and riffles. Gold content of five size fractions finer than 212 μm was determined by fire-assay atomic absorption spectrometry (FA-AAS). Use of a bulk leach cyanidation (BLC) procedure for Au was also tested. A strong, but extremely erratic, Au anomaly is present in the sand-size fractions. The highest Au values are found in gravel environments and concentrations decrease with decreasing grain size to a minimum in the −53 μm fraction. To maintain anomaly contrast while minimizing the chance of missing the erratic Au anomalies, use of the −105 μm fraction is recommended for stream sediment exploration surveys in the region. Because of the abundance and low Au content of the −53 μm fraction, increased erosion and additional inputs of this fraction into the stream after logging activity could significantly dilute the Au anomaly.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".